Top Artificial Intelligence Jobs in Raleigh-Durham, NC

Design and ship intuitive UX for AI-powered recruiting products, including agentic workflows, agent artifacts, customer prototypes, design systems, and SaaS experiences. Collaborate closely with Product, Engineering, and customers from concept through production. Contribute front-end polish through React and TypeScript pull requests, covering responsive behavior, accessibility, motion, and interface states.

Own product strategy, roadmap, platform design, packaging, pricing, and product processes as Klarify’s first product leader. Partner with the CEO and CTO on prioritization, set adoption, retention, and revenue goals, engage therapists, oversee support, incorporate privacy into product decisions, support investor storytelling, and personally ship prototypes, internal tools, and production code.

Lead Dwellci AI’s technical vision, engineering organization, AI model development, and scalable 3D generative design platform. The CTO will establish architecture and engineering practices, build and mentor an AI team, guide product innovation, collaborate with founders and designers, engage investors, and advance generative AI, computer vision, and computational design capabilities for architecture.

Provides advanced technical support for robotic systems during commissioning and 24/7 customer operations. Diagnoses hardware, software, controls, Linux, network, and automation failures; analyzes logs and telemetry; develops integrations and support workflows; leads major incidents; coordinates engineering, field service, and customer teams; manages cases and corrective actions; validates AI-assisted diagnostics; and mentors engineers while improving system reliability and recovery times.

Own commercial relationships across a global partner portfolio, driving renewals, upselling, cross-selling, partner acquisition, enablement, joint selling, business reviews, pipeline management, and forecasting. The role collaborates with technical customer success, Product, Engineering, and channel leadership while representing partner needs internally. Success is measured by revenue growth, partner engagement, retention, adoption, and pipeline contribution.

Owns customer relationships from onboarding through adoption, expansion, and renewal. Maps workflows and data, identifies high-value AI opportunities, translates needs into implementation requirements, partners with product and engineering on deployments, leads rollout and adoption, tracks customer value, and develops repeatable customer success and deployment playbooks.

Leads defined contribution retirement plan administration workflows, quality assurance, compliance testing, filings, distributions, and complex escalations. Reviews team work, oversees regulatory audits and QDRO processing, improves operational processes, maintains documentation, and identifies automation opportunities. Partners with clients, recordkeepers, relationship managers, and internal teams to ensure accurate, timely service and compliance with IRS and DOL requirements.

Conduct outbound sales to US technology leaders through cold calls, email, LinkedIn, conferences, and personalized prospecting sequences. Own the pipeline through qualified meeting booking, build targeted prospect lists, maintain CRM records, and hand prospects to the partnerships director. The role also involves developing a professional LinkedIn presence, testing messaging and channels, and selling custom software development services to technical buyers.

Owns lifecycle and engagement marketing for health benefits members across email, SMS, direct mail, print, digital, landing pages, and inbound flows. Builds segmentation, campaigns, testing programs, client engagement playbooks, and compliant Medicare communications. Manages engagement and enrollment funnels, marketing automation, CRM data flows, dashboards, and collaboration with agents, Customer Success, design, and product teams.

Train and deploy manipulation policies for humanoid robots using teleoperation data. Responsibilities include VLA and imitation-learning model training, cross-embodiment foundation-model development, distributed training, evaluation through simulation and real-robot trials, dataset quality tooling, policy deployment, and failure analysis.